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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Alan Saul 2015-09-24 13:45:00 +01:00
commit 98c885c486
3 changed files with 34 additions and 10 deletions
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24
GPy/kern/_src/add.py
View file

@ -4,7 +4,7 @@
import numpy as np
import itertools
from ...util.caching import Cache_this
from .kern import CombinationKernel
from .kern import CombinationKernel, Kern
from functools import reduce
class Add(CombinationKernel):
@ -21,8 +21,21 @@ class Add(CombinationKernel):
for part in kern.parts[::-1]:
kern.unlink_parameter(part)
subkerns.insert(i, part)
super(Add, self).__init__(subkerns, name)
self._exact_psicomp = self._check_exact_psicomp()
def _check_exact_psicomp(self):
from .. import RBF,Linear,Bias,White
n_kerns = len(self.parts)
n_rbf = len([k for k in self.parts if isinstance(k,RBF)])
n_linear = len([k for k in self.parts if isinstance(k,Linear)])
n_bias = len([k for k in self.parts if isinstance(k,Bias)])
n_white = len([k for k in self.parts if isinstance(k,White)])
n_others = n_kerns - n_rbf - n_linear - n_bias - n_white
if n_rbf+n_linear<=1 and n_bias<=1 and n_white<=1 and n_others==0:
return True
else:
return False
@Cache_this(limit=2, force_kwargs=['which_parts'])
def K(self, X, X2=None, which_parts=None):
@ -87,14 +100,17 @@ class Add(CombinationKernel):
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi0(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi0(self,Z,variational_posterior)
return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi1(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi1(self,Z,variational_posterior)
return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi2(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi2(self,Z,variational_posterior)
psi2 = reduce(np.add, (p.psi2(Z, variational_posterior) for p in self.parts))
#return psi2
# compute the "cross" terms
@ -130,6 +146,7 @@ class Add(CombinationKernel):
@Cache_this(limit=1, force_kwargs=['which_parts'])
def psi2n(self, Z, variational_posterior):
if not self._exact_psicomp: return Kern.psi2n(self, Z, variational_posterior)
psi2 = reduce(np.add, (p.psi2n(Z, variational_posterior) for p in self.parts))
#return psi2
# compute the "cross" terms
@ -164,6 +181,7 @@ class Add(CombinationKernel):
return psi2
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
if not self._exact_psicomp: return Kern.update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
from .static import White, Bias
for p1 in self.parts:
#compute the effective dL_dpsi1. Extra terms appear becaue of the cross terms in psi2!
@ -180,6 +198,7 @@ class Add(CombinationKernel):
p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, Z, variational_posterior)
def gradients_Z_expectations(self, dL_psi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
if not self._exact_psicomp: return Kern.gradients_Z_expectations(self, dL_psi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
from .static import White, Bias
target = np.zeros(Z.shape)
for p1 in self.parts:
@ -198,6 +217,7 @@ class Add(CombinationKernel):
return target
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
if not self._exact_psicomp: return Kern.gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior)
from .static import White, Bias
target_grads = [np.zeros(v.shape) for v in variational_posterior.parameters]
for p1 in self.parameters:

15
GPy/kern/_src/psi_comp/gaussherm.py
View file

@ -13,9 +13,6 @@ from ....util.linalg import tdot
from . import PSICOMP
class PSICOMP_GH(PSICOMP):
"""
TODO: support Psi2 with shape NxMxM
"""
def __init__(self, degree=5, cache_K=True):
self.degree = degree
@ -48,7 +45,7 @@ class PSICOMP_GH(PSICOMP):
psi0 = np.zeros((N,))
psi1 = np.zeros((N,M))
psi2 = np.zeros((M,M))
psi2 = np.zeros((N,M,M)) if return_psi2_n else np.zeros((M,M))
for i in xrange(self.degree):
if self.cache_K:
X = Xs[i]
@ -57,7 +54,10 @@ class PSICOMP_GH(PSICOMP):
psi0 += self.weights[i]* kern.Kdiag(X)
Kfu = kern.K(X,Z)
psi1 += self.weights[i]* Kfu
psi2 += self.weights[i]* tdot(Kfu.T)
if return_psi2_n:
psi2 += self.weights[i]* Kfu[:,None,:]*Kfu[:,:,None]
else:
psi2 += self.weights[i]* tdot(Kfu.T)
return psi0, psi1, psi2
@Cache_this(limit=10, ignore_args=(0, 2,3,4))
@ -84,7 +84,10 @@ class PSICOMP_GH(PSICOMP):
dtheta += kern.gradient
dX = kern.gradients_X_diag(dL_dpsi0_i, X)
Kfu = kern.K(X,Z)
dL_dkfu = (dL_dpsi1+ 2.*Kfu.dot(dL_dpsi2))*self.weights[i]
if len(dL_dpsi2.shape)==2:
dL_dkfu = (dL_dpsi1+ Kfu.dot(dL_dpsi2+dL_dpsi2.T))*self.weights[i]
else:
dL_dkfu = (dL_dpsi1+ (Kfu[:,:,None]*(dL_dpsi2+np.swapaxes(dL_dpsi2, 1,2))).sum(1))*self.weights[i]
kern.update_gradients_full(dL_dkfu, X, Z)
dtheta += kern.gradient
dX_i, dZ_i = kern.gradients_X_X2(dL_dkfu, X, Z)

5
GPy/testing/kernel_tests.py
View file

@ -469,9 +469,10 @@ class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
self.w3n = self.w3n+np.swapaxes(self.w3n, 1,2)
def test_kernels(self):
from GPy.kern import RBF,Linear,MLP
from GPy.kern import RBF,Linear,MLP,Bias,White
Q = self.Z.shape[1]
kernels = [RBF(Q,ARD=True), Linear(Q,ARD=True)]
kernels = [RBF(Q,ARD=True), Linear(Q,ARD=True),MLP(Q,ARD=True), RBF(Q,ARD=True)+Linear(Q,ARD=True)+Bias(Q)+White(Q)
,RBF(Q,ARD=True)+Bias(Q)+White(Q), Linear(Q,ARD=True)+Bias(Q)+White(Q)]
for k in kernels:
k.randomize()